In the field of Optical Character Recognition (OCR), many matching features are sensitive to the slant of the characters. For instance, if a system expects an "I" to be a vertical line, then it may very well fail to recognize an "I" that has a 45 Degree slant. Furthermore character slant varies between different fonts, the handwriting of different people, and even different instances of handwriting of the same person, creating significant problems in learning character templates, and matching new input to existing templates. Character slant becomes an even bigger problem when dealing with whole words in which individual characters are to be separated automatically. The slant may cause vertical overlap between consecutive characters, thus making separation much more error prone.
There is a known method, proposed by R. G. Casey in an article titled "Moment Normalization of Handprinted Characters" published on IBM Journal of Research and Development, September 1970, for correcting the character distortions. This method uses global correlation between X and Y values of pixels for detecting the character slant. The method is based on transforming the input image so that the XY moment around the image's centroid is 0. The method reportedly works well for single characters, but when dealing with complete words, and not single characters, it frequently fails. A word may be skewed without relation to the slant: a person can write perfectly straight characters, but each one a bit higher (or lower) than the previous one. In such cases the global skew severely distorts the slant computed for individual characters, thus making global correlation methods unsuitable for processing more than a single character at a time, and in particular for separating characters in handwritten words. In addition to this problem, some single characters, such as L, have non-zero correlation between X and Y values, and a false slant is detected by global correlation methods. In processing whole words this problem is severely magnified.